As of January 1, 2020 this library no longer supports Python 2 on the latest released version.
Library versions released prior to that date will continue to be available. For more information please
visit Python 2 support on Google Cloud.
Source code for google.ai.generativelanguage_v1.types.generative_service
# -*- coding: utf-8 -*-
# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from __future__ import annotations
from typing import MutableMapping, MutableSequence
import google.protobuf.struct_pb2 as struct_pb2 # type: ignore
import proto # type: ignore
from google.ai.generativelanguage_v1.types import citation, safety
from google.ai.generativelanguage_v1.types import content as gag_content
__protobuf__ = proto.module(
package="google.ai.generativelanguage.v1",
manifest={
"TaskType",
"GenerateContentRequest",
"GenerationConfig",
"GenerateContentResponse",
"Candidate",
"UrlContextMetadata",
"UrlMetadata",
"LogprobsResult",
"RetrievalMetadata",
"GroundingMetadata",
"SearchEntryPoint",
"GroundingChunk",
"Segment",
"GroundingSupport",
"EmbedContentRequest",
"ContentEmbedding",
"EmbedContentResponse",
"BatchEmbedContentsRequest",
"BatchEmbedContentsResponse",
"CountTokensRequest",
"CountTokensResponse",
},
)
[docs]class TaskType(proto.Enum):
r"""Type of task for which the embedding will be used.
Values:
TASK_TYPE_UNSPECIFIED (0):
Unset value, which will default to one of the
other enum values.
RETRIEVAL_QUERY (1):
Specifies the given text is a query in a
search/retrieval setting.
RETRIEVAL_DOCUMENT (2):
Specifies the given text is a document from
the corpus being searched.
SEMANTIC_SIMILARITY (3):
Specifies the given text will be used for
STS.
CLASSIFICATION (4):
Specifies that the given text will be
classified.
CLUSTERING (5):
Specifies that the embeddings will be used
for clustering.
QUESTION_ANSWERING (6):
Specifies that the given text will be used
for question answering.
FACT_VERIFICATION (7):
Specifies that the given text will be used
for fact verification.
CODE_RETRIEVAL_QUERY (8):
Specifies that the given text will be used
for code retrieval.
"""
TASK_TYPE_UNSPECIFIED = 0
RETRIEVAL_QUERY = 1
RETRIEVAL_DOCUMENT = 2
SEMANTIC_SIMILARITY = 3
CLASSIFICATION = 4
CLUSTERING = 5
QUESTION_ANSWERING = 6
FACT_VERIFICATION = 7
CODE_RETRIEVAL_QUERY = 8
[docs]class GenerateContentRequest(proto.Message):
r"""Request to generate a completion from the model.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
Attributes:
model (str):
Required. The name of the ``Model`` to use for generating
the completion.
Format: ``models/{model}``.
contents (MutableSequence[google.ai.generativelanguage_v1.types.Content]):
Required. The content of the current conversation with the
model.
For single-turn queries, this is a single instance. For
multi-turn queries like
`chat <https://ai.google.dev/gemini-api/docs/text-generation#chat>`__,
this is a repeated field that contains the conversation
history and the latest request.
safety_settings (MutableSequence[google.ai.generativelanguage_v1.types.SafetySetting]):
Optional. A list of unique ``SafetySetting`` instances for
blocking unsafe content.
This will be enforced on the
``GenerateContentRequest.contents`` and
``GenerateContentResponse.candidates``. There should not be
more than one setting for each ``SafetyCategory`` type. The
API will block any contents and responses that fail to meet
the thresholds set by these settings. This list overrides
the default settings for each ``SafetyCategory`` specified
in the safety_settings. If there is no ``SafetySetting`` for
a given ``SafetyCategory`` provided in the list, the API
will use the default safety setting for that category. Harm
categories HARM_CATEGORY_HATE_SPEECH,
HARM_CATEGORY_SEXUALLY_EXPLICIT,
HARM_CATEGORY_DANGEROUS_CONTENT, HARM_CATEGORY_HARASSMENT,
HARM_CATEGORY_CIVIC_INTEGRITY are supported. Refer to the
`guide <https://ai.google.dev/gemini-api/docs/safety-settings>`__
for detailed information on available safety settings. Also
refer to the `Safety
guidance <https://ai.google.dev/gemini-api/docs/safety-guidance>`__
to learn how to incorporate safety considerations in your AI
applications.
generation_config (google.ai.generativelanguage_v1.types.GenerationConfig):
Optional. Configuration options for model
generation and outputs.
This field is a member of `oneof`_ ``_generation_config``.
"""
model: str = proto.Field(
proto.STRING,
number=1,
)
contents: MutableSequence[gag_content.Content] = proto.RepeatedField(
proto.MESSAGE,
number=2,
message=gag_content.Content,
)
safety_settings: MutableSequence[safety.SafetySetting] = proto.RepeatedField(
proto.MESSAGE,
number=3,
message=safety.SafetySetting,
)
generation_config: "GenerationConfig" = proto.Field(
proto.MESSAGE,
number=4,
optional=True,
message="GenerationConfig",
)
[docs]class GenerationConfig(proto.Message):
r"""Configuration options for model generation and outputs. Not
all parameters are configurable for every model.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
Attributes:
candidate_count (int):
Optional. Number of generated responses to
return. If unset, this will default to 1. Please
note that this doesn't work for previous
generation models (Gemini 1.0 family)
This field is a member of `oneof`_ ``_candidate_count``.
stop_sequences (MutableSequence[str]):
Optional. The set of character sequences (up to 5) that will
stop output generation. If specified, the API will stop at
the first appearance of a ``stop_sequence``. The stop
sequence will not be included as part of the response.
max_output_tokens (int):
Optional. The maximum number of tokens to include in a
response candidate.
Note: The default value varies by model, see the
``Model.output_token_limit`` attribute of the ``Model``
returned from the ``getModel`` function.
This field is a member of `oneof`_ ``_max_output_tokens``.
temperature (float):
Optional. Controls the randomness of the output.
Note: The default value varies by model, see the
``Model.temperature`` attribute of the ``Model`` returned
from the ``getModel`` function.
Values can range from [0.0, 2.0].
This field is a member of `oneof`_ ``_temperature``.
top_p (float):
Optional. The maximum cumulative probability of tokens to
consider when sampling.
The model uses combined Top-k and Top-p (nucleus) sampling.
Tokens are sorted based on their assigned probabilities so
that only the most likely tokens are considered. Top-k
sampling directly limits the maximum number of tokens to
consider, while Nucleus sampling limits the number of tokens
based on the cumulative probability.
Note: The default value varies by ``Model`` and is specified
by the\ ``Model.top_p`` attribute returned from the
``getModel`` function. An empty ``top_k`` attribute
indicates that the model doesn't apply top-k sampling and
doesn't allow setting ``top_k`` on requests.
This field is a member of `oneof`_ ``_top_p``.
top_k (int):
Optional. The maximum number of tokens to consider when
sampling.
Gemini models use Top-p (nucleus) sampling or a combination
of Top-k and nucleus sampling. Top-k sampling considers the
set of ``top_k`` most probable tokens. Models running with
nucleus sampling don't allow top_k setting.
Note: The default value varies by ``Model`` and is specified
by the\ ``Model.top_p`` attribute returned from the
``getModel`` function. An empty ``top_k`` attribute
indicates that the model doesn't apply top-k sampling and
doesn't allow setting ``top_k`` on requests.
This field is a member of `oneof`_ ``_top_k``.
seed (int):
Optional. Seed used in decoding. If not set,
the request uses a randomly generated seed.
This field is a member of `oneof`_ ``_seed``.
response_json_schema_ordered (google.protobuf.struct_pb2.Value):
Optional. An internal detail. Use ``responseJsonSchema``
rather than this field.
presence_penalty (float):
Optional. Presence penalty applied to the next token's
logprobs if the token has already been seen in the response.
This penalty is binary on/off and not dependant on the
number of times the token is used (after the first). Use
[frequency_penalty][google.ai.generativelanguage.v1.GenerationConfig.frequency_penalty]
for a penalty that increases with each use.
A positive penalty will discourage the use of tokens that
have already been used in the response, increasing the
vocabulary.
A negative penalty will encourage the use of tokens that
have already been used in the response, decreasing the
vocabulary.
This field is a member of `oneof`_ ``_presence_penalty``.
frequency_penalty (float):
Optional. Frequency penalty applied to the next token's
logprobs, multiplied by the number of times each token has
been seen in the respponse so far.
A positive penalty will discourage the use of tokens that
have already been used, proportional to the number of times
the token has been used: The more a token is used, the more
difficult it is for the model to use that token again
increasing the vocabulary of responses.
Caution: A *negative* penalty will encourage the model to
reuse tokens proportional to the number of times the token
has been used. Small negative values will reduce the
vocabulary of a response. Larger negative values will cause
the model to start repeating a common token until it hits
the
[max_output_tokens][google.ai.generativelanguage.v1.GenerationConfig.max_output_tokens]
limit.
This field is a member of `oneof`_ ``_frequency_penalty``.
response_logprobs (bool):
Optional. If true, export the logprobs
results in response.
This field is a member of `oneof`_ ``_response_logprobs``.
logprobs (int):
Optional. Only valid if
[response_logprobs=True][google.ai.generativelanguage.v1.GenerationConfig.response_logprobs].
This sets the number of top logprobs to return at each
decoding step in the
[Candidate.logprobs_result][google.ai.generativelanguage.v1.Candidate.logprobs_result].
The number must be in the range of [0, 20].
This field is a member of `oneof`_ ``_logprobs``.
enable_enhanced_civic_answers (bool):
Optional. Enables enhanced civic answers. It
may not be available for all models.
This field is a member of `oneof`_ ``_enable_enhanced_civic_answers``.
"""
candidate_count: int = proto.Field(
proto.INT32,
number=1,
optional=True,
)
stop_sequences: MutableSequence[str] = proto.RepeatedField(
proto.STRING,
number=2,
)
max_output_tokens: int = proto.Field(
proto.INT32,
number=4,
optional=True,
)
temperature: float = proto.Field(
proto.FLOAT,
number=5,
optional=True,
)
top_p: float = proto.Field(
proto.FLOAT,
number=6,
optional=True,
)
top_k: int = proto.Field(
proto.INT32,
number=7,
optional=True,
)
seed: int = proto.Field(
proto.INT32,
number=8,
optional=True,
)
response_json_schema_ordered: struct_pb2.Value = proto.Field(
proto.MESSAGE,
number=28,
message=struct_pb2.Value,
)
presence_penalty: float = proto.Field(
proto.FLOAT,
number=15,
optional=True,
)
frequency_penalty: float = proto.Field(
proto.FLOAT,
number=16,
optional=True,
)
response_logprobs: bool = proto.Field(
proto.BOOL,
number=17,
optional=True,
)
logprobs: int = proto.Field(
proto.INT32,
number=18,
optional=True,
)
enable_enhanced_civic_answers: bool = proto.Field(
proto.BOOL,
number=19,
optional=True,
)
[docs]class GenerateContentResponse(proto.Message):
r"""Response from the model supporting multiple candidate responses.
Safety ratings and content filtering are reported for both prompt in
``GenerateContentResponse.prompt_feedback`` and for each candidate
in ``finish_reason`` and in ``safety_ratings``. The API:
- Returns either all requested candidates or none of them
- Returns no candidates at all only if there was something wrong
with the prompt (check ``prompt_feedback``)
- Reports feedback on each candidate in ``finish_reason`` and
``safety_ratings``.
Attributes:
candidates (MutableSequence[google.ai.generativelanguage_v1.types.Candidate]):
Candidate responses from the model.
prompt_feedback (google.ai.generativelanguage_v1.types.GenerateContentResponse.PromptFeedback):
Returns the prompt's feedback related to the
content filters.
usage_metadata (google.ai.generativelanguage_v1.types.GenerateContentResponse.UsageMetadata):
Output only. Metadata on the generation
requests' token usage.
model_version (str):
Output only. The model version used to
generate the response.
response_id (str):
Output only. response_id is used to identify each response.
"""
[docs] class PromptFeedback(proto.Message):
r"""A set of the feedback metadata the prompt specified in
``GenerateContentRequest.content``.
Attributes:
block_reason (google.ai.generativelanguage_v1.types.GenerateContentResponse.PromptFeedback.BlockReason):
Optional. If set, the prompt was blocked and
no candidates are returned. Rephrase the prompt.
safety_ratings (MutableSequence[google.ai.generativelanguage_v1.types.SafetyRating]):
Ratings for safety of the prompt.
There is at most one rating per category.
"""
[docs] class BlockReason(proto.Enum):
r"""Specifies the reason why the prompt was blocked.
Values:
BLOCK_REASON_UNSPECIFIED (0):
Default value. This value is unused.
SAFETY (1):
Prompt was blocked due to safety reasons. Inspect
``safety_ratings`` to understand which safety category
blocked it.
OTHER (2):
Prompt was blocked due to unknown reasons.
BLOCKLIST (3):
Prompt was blocked due to the terms which are
included from the terminology blocklist.
PROHIBITED_CONTENT (4):
Prompt was blocked due to prohibited content.
IMAGE_SAFETY (5):
Candidates blocked due to unsafe image
generation content.
"""
BLOCK_REASON_UNSPECIFIED = 0
SAFETY = 1
OTHER = 2
BLOCKLIST = 3
PROHIBITED_CONTENT = 4
IMAGE_SAFETY = 5
block_reason: "GenerateContentResponse.PromptFeedback.BlockReason" = (
proto.Field(
proto.ENUM,
number=1,
enum="GenerateContentResponse.PromptFeedback.BlockReason",
)
)
safety_ratings: MutableSequence[safety.SafetyRating] = proto.RepeatedField(
proto.MESSAGE,
number=2,
message=safety.SafetyRating,
)
[docs] class UsageMetadata(proto.Message):
r"""Metadata on the generation request's token usage.
Attributes:
prompt_token_count (int):
Number of tokens in the prompt. When ``cached_content`` is
set, this is still the total effective prompt size meaning
this includes the number of tokens in the cached content.
candidates_token_count (int):
Total number of tokens across all the
generated response candidates.
tool_use_prompt_token_count (int):
Output only. Number of tokens present in
tool-use prompt(s).
thoughts_token_count (int):
Output only. Number of tokens of thoughts for
thinking models.
total_token_count (int):
Total token count for the generation request
(prompt + response candidates).
prompt_tokens_details (MutableSequence[google.ai.generativelanguage_v1.types.ModalityTokenCount]):
Output only. List of modalities that were
processed in the request input.
cache_tokens_details (MutableSequence[google.ai.generativelanguage_v1.types.ModalityTokenCount]):
Output only. List of modalities of the cached
content in the request input.
candidates_tokens_details (MutableSequence[google.ai.generativelanguage_v1.types.ModalityTokenCount]):
Output only. List of modalities that were
returned in the response.
tool_use_prompt_tokens_details (MutableSequence[google.ai.generativelanguage_v1.types.ModalityTokenCount]):
Output only. List of modalities that were
processed for tool-use request inputs.
"""
prompt_token_count: int = proto.Field(
proto.INT32,
number=1,
)
candidates_token_count: int = proto.Field(
proto.INT32,
number=2,
)
tool_use_prompt_token_count: int = proto.Field(
proto.INT32,
number=8,
)
thoughts_token_count: int = proto.Field(
proto.INT32,
number=10,
)
total_token_count: int = proto.Field(
proto.INT32,
number=3,
)
prompt_tokens_details: MutableSequence[gag_content.ModalityTokenCount] = (
proto.RepeatedField(
proto.MESSAGE,
number=5,
message=gag_content.ModalityTokenCount,
)
)
cache_tokens_details: MutableSequence[gag_content.ModalityTokenCount] = (
proto.RepeatedField(
proto.MESSAGE,
number=6,
message=gag_content.ModalityTokenCount,
)
)
candidates_tokens_details: MutableSequence[gag_content.ModalityTokenCount] = (
proto.RepeatedField(
proto.MESSAGE,
number=7,
message=gag_content.ModalityTokenCount,
)
)
tool_use_prompt_tokens_details: MutableSequence[
gag_content.ModalityTokenCount
] = proto.RepeatedField(
proto.MESSAGE,
number=9,
message=gag_content.ModalityTokenCount,
)
candidates: MutableSequence["Candidate"] = proto.RepeatedField(
proto.MESSAGE,
number=1,
message="Candidate",
)
prompt_feedback: PromptFeedback = proto.Field(
proto.MESSAGE,
number=2,
message=PromptFeedback,
)
usage_metadata: UsageMetadata = proto.Field(
proto.MESSAGE,
number=3,
message=UsageMetadata,
)
model_version: str = proto.Field(
proto.STRING,
number=4,
)
response_id: str = proto.Field(
proto.STRING,
number=5,
)
[docs]class Candidate(proto.Message):
r"""A response candidate generated from the model.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
Attributes:
index (int):
Output only. Index of the candidate in the
list of response candidates.
This field is a member of `oneof`_ ``_index``.
content (google.ai.generativelanguage_v1.types.Content):
Output only. Generated content returned from
the model.
finish_reason (google.ai.generativelanguage_v1.types.Candidate.FinishReason):
Optional. Output only. The reason why the
model stopped generating tokens.
If empty, the model has not stopped generating
tokens.
finish_message (str):
Optional. Output only. Details the reason why the model
stopped generating tokens. This is populated only when
``finish_reason`` is set.
This field is a member of `oneof`_ ``_finish_message``.
safety_ratings (MutableSequence[google.ai.generativelanguage_v1.types.SafetyRating]):
List of ratings for the safety of a response
candidate.
There is at most one rating per category.
citation_metadata (google.ai.generativelanguage_v1.types.CitationMetadata):
Output only. Citation information for model-generated
candidate.
This field may be populated with recitation information for
any text included in the ``content``. These are passages
that are "recited" from copyrighted material in the
foundational LLM's training data.
token_count (int):
Output only. Token count for this candidate.
grounding_metadata (google.ai.generativelanguage_v1.types.GroundingMetadata):
Output only. Grounding metadata for the candidate.
This field is populated for ``GenerateContent`` calls.
avg_logprobs (float):
Output only. Average log probability score of
the candidate.
logprobs_result (google.ai.generativelanguage_v1.types.LogprobsResult):
Output only. Log-likelihood scores for the
response tokens and top tokens
url_context_metadata (google.ai.generativelanguage_v1.types.UrlContextMetadata):
Output only. Metadata related to url context
retrieval tool.
"""
[docs] class FinishReason(proto.Enum):
r"""Defines the reason why the model stopped generating tokens.
Values:
FINISH_REASON_UNSPECIFIED (0):
Default value. This value is unused.
STOP (1):
Natural stop point of the model or provided
stop sequence.
MAX_TOKENS (2):
The maximum number of tokens as specified in
the request was reached.
SAFETY (3):
The response candidate content was flagged
for safety reasons.
RECITATION (4):
The response candidate content was flagged
for recitation reasons.
LANGUAGE (6):
The response candidate content was flagged
for using an unsupported language.
OTHER (5):
Unknown reason.
BLOCKLIST (7):
Token generation stopped because the content
contains forbidden terms.
PROHIBITED_CONTENT (8):
Token generation stopped for potentially
containing prohibited content.
SPII (9):
Token generation stopped because the content
potentially contains Sensitive Personally
Identifiable Information (SPII).
MALFORMED_FUNCTION_CALL (10):
The function call generated by the model is
invalid.
IMAGE_SAFETY (11):
Token generation stopped because generated
images contain safety violations.
IMAGE_PROHIBITED_CONTENT (14):
Image generation stopped because generated
images has other prohibited content.
IMAGE_OTHER (15):
Image generation stopped because of other
miscellaneous issue.
NO_IMAGE (16):
The model was expected to generate an image,
but none was generated.
IMAGE_RECITATION (17):
Image generation stopped due to recitation.
UNEXPECTED_TOOL_CALL (12):
Model generated a tool call but no tools were
enabled in the request.
TOO_MANY_TOOL_CALLS (13):
Model called too many tools consecutively,
thus the system exited execution.
"""
FINISH_REASON_UNSPECIFIED = 0
STOP = 1
MAX_TOKENS = 2
SAFETY = 3
RECITATION = 4
LANGUAGE = 6
OTHER = 5
BLOCKLIST = 7
PROHIBITED_CONTENT = 8
SPII = 9
MALFORMED_FUNCTION_CALL = 10
IMAGE_SAFETY = 11
IMAGE_PROHIBITED_CONTENT = 14
IMAGE_OTHER = 15
NO_IMAGE = 16
IMAGE_RECITATION = 17
UNEXPECTED_TOOL_CALL = 12
TOO_MANY_TOOL_CALLS = 13
index: int = proto.Field(
proto.INT32,
number=3,
optional=True,
)
content: gag_content.Content = proto.Field(
proto.MESSAGE,
number=1,
message=gag_content.Content,
)
finish_reason: FinishReason = proto.Field(
proto.ENUM,
number=2,
enum=FinishReason,
)
finish_message: str = proto.Field(
proto.STRING,
number=4,
optional=True,
)
safety_ratings: MutableSequence[safety.SafetyRating] = proto.RepeatedField(
proto.MESSAGE,
number=5,
message=safety.SafetyRating,
)
citation_metadata: citation.CitationMetadata = proto.Field(
proto.MESSAGE,
number=6,
message=citation.CitationMetadata,
)
token_count: int = proto.Field(
proto.INT32,
number=7,
)
grounding_metadata: "GroundingMetadata" = proto.Field(
proto.MESSAGE,
number=9,
message="GroundingMetadata",
)
avg_logprobs: float = proto.Field(
proto.DOUBLE,
number=10,
)
logprobs_result: "LogprobsResult" = proto.Field(
proto.MESSAGE,
number=11,
message="LogprobsResult",
)
url_context_metadata: "UrlContextMetadata" = proto.Field(
proto.MESSAGE,
number=13,
message="UrlContextMetadata",
)
[docs]class UrlContextMetadata(proto.Message):
r"""Metadata related to url context retrieval tool.
Attributes:
url_metadata (MutableSequence[google.ai.generativelanguage_v1.types.UrlMetadata]):
List of url context.
"""
url_metadata: MutableSequence["UrlMetadata"] = proto.RepeatedField(
proto.MESSAGE,
number=1,
message="UrlMetadata",
)
[docs]class UrlMetadata(proto.Message):
r"""Context of the a single url retrieval.
Attributes:
retrieved_url (str):
Retrieved url by the tool.
url_retrieval_status (google.ai.generativelanguage_v1.types.UrlMetadata.UrlRetrievalStatus):
Status of the url retrieval.
"""
[docs] class UrlRetrievalStatus(proto.Enum):
r"""Status of the url retrieval.
Values:
URL_RETRIEVAL_STATUS_UNSPECIFIED (0):
Default value. This value is unused.
URL_RETRIEVAL_STATUS_SUCCESS (1):
Url retrieval is successful.
URL_RETRIEVAL_STATUS_ERROR (2):
Url retrieval is failed due to error.
URL_RETRIEVAL_STATUS_PAYWALL (3):
Url retrieval is failed because the content
is behind paywall.
URL_RETRIEVAL_STATUS_UNSAFE (4):
Url retrieval is failed because the content
is unsafe.
"""
URL_RETRIEVAL_STATUS_UNSPECIFIED = 0
URL_RETRIEVAL_STATUS_SUCCESS = 1
URL_RETRIEVAL_STATUS_ERROR = 2
URL_RETRIEVAL_STATUS_PAYWALL = 3
URL_RETRIEVAL_STATUS_UNSAFE = 4
retrieved_url: str = proto.Field(
proto.STRING,
number=1,
)
url_retrieval_status: UrlRetrievalStatus = proto.Field(
proto.ENUM,
number=2,
enum=UrlRetrievalStatus,
)
[docs]class LogprobsResult(proto.Message):
r"""Logprobs Result
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
Attributes:
log_probability_sum (float):
Sum of log probabilities for all tokens.
This field is a member of `oneof`_ ``_log_probability_sum``.
top_candidates (MutableSequence[google.ai.generativelanguage_v1.types.LogprobsResult.TopCandidates]):
Length = total number of decoding steps.
chosen_candidates (MutableSequence[google.ai.generativelanguage_v1.types.LogprobsResult.Candidate]):
Length = total number of decoding steps. The chosen
candidates may or may not be in top_candidates.
"""
[docs] class Candidate(proto.Message):
r"""Candidate for the logprobs token and score.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
Attributes:
token (str):
The candidate’s token string value.
This field is a member of `oneof`_ ``_token``.
token_id (int):
The candidate’s token id value.
This field is a member of `oneof`_ ``_token_id``.
log_probability (float):
The candidate's log probability.
This field is a member of `oneof`_ ``_log_probability``.
"""
token: str = proto.Field(
proto.STRING,
number=1,
optional=True,
)
token_id: int = proto.Field(
proto.INT32,
number=3,
optional=True,
)
log_probability: float = proto.Field(
proto.FLOAT,
number=2,
optional=True,
)
[docs] class TopCandidates(proto.Message):
r"""Candidates with top log probabilities at each decoding step.
Attributes:
candidates (MutableSequence[google.ai.generativelanguage_v1.types.LogprobsResult.Candidate]):
Sorted by log probability in descending
order.
"""
candidates: MutableSequence["LogprobsResult.Candidate"] = proto.RepeatedField(
proto.MESSAGE,
number=1,
message="LogprobsResult.Candidate",
)
log_probability_sum: float = proto.Field(
proto.FLOAT,
number=3,
optional=True,
)
top_candidates: MutableSequence[TopCandidates] = proto.RepeatedField(
proto.MESSAGE,
number=1,
message=TopCandidates,
)
chosen_candidates: MutableSequence[Candidate] = proto.RepeatedField(
proto.MESSAGE,
number=2,
message=Candidate,
)
[docs]class RetrievalMetadata(proto.Message):
r"""Metadata related to retrieval in the grounding flow.
Attributes:
google_search_dynamic_retrieval_score (float):
Optional. Score indicating how likely information from
google search could help answer the prompt. The score is in
the range [0, 1], where 0 is the least likely and 1 is the
most likely. This score is only populated when google search
grounding and dynamic retrieval is enabled. It will be
compared to the threshold to determine whether to trigger
google search.
"""
google_search_dynamic_retrieval_score: float = proto.Field(
proto.FLOAT,
number=2,
)
[docs]class GroundingMetadata(proto.Message):
r"""Metadata returned to client when grounding is enabled.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
Attributes:
search_entry_point (google.ai.generativelanguage_v1.types.SearchEntryPoint):
Optional. Google search entry for the
following-up web searches.
This field is a member of `oneof`_ ``_search_entry_point``.
grounding_chunks (MutableSequence[google.ai.generativelanguage_v1.types.GroundingChunk]):
List of supporting references retrieved from
specified grounding source.
grounding_supports (MutableSequence[google.ai.generativelanguage_v1.types.GroundingSupport]):
List of grounding support.
retrieval_metadata (google.ai.generativelanguage_v1.types.RetrievalMetadata):
Metadata related to retrieval in the
grounding flow.
This field is a member of `oneof`_ ``_retrieval_metadata``.
web_search_queries (MutableSequence[str]):
Web search queries for the following-up web
search.
google_maps_widget_context_token (str):
Optional. Resource name of the Google Maps
widget context token that can be used with the
PlacesContextElement widget in order to render
contextual data. Only populated in the case that
grounding with Google Maps is enabled.
This field is a member of `oneof`_ ``_google_maps_widget_context_token``.
"""
search_entry_point: "SearchEntryPoint" = proto.Field(
proto.MESSAGE,
number=1,
optional=True,
message="SearchEntryPoint",
)
grounding_chunks: MutableSequence["GroundingChunk"] = proto.RepeatedField(
proto.MESSAGE,
number=2,
message="GroundingChunk",
)
grounding_supports: MutableSequence["GroundingSupport"] = proto.RepeatedField(
proto.MESSAGE,
number=3,
message="GroundingSupport",
)
retrieval_metadata: "RetrievalMetadata" = proto.Field(
proto.MESSAGE,
number=4,
optional=True,
message="RetrievalMetadata",
)
web_search_queries: MutableSequence[str] = proto.RepeatedField(
proto.STRING,
number=5,
)
google_maps_widget_context_token: str = proto.Field(
proto.STRING,
number=7,
optional=True,
)
[docs]class SearchEntryPoint(proto.Message):
r"""Google search entry point.
Attributes:
rendered_content (str):
Optional. Web content snippet that can be
embedded in a web page or an app webview.
sdk_blob (bytes):
Optional. Base64 encoded JSON representing
array of <search term, search url> tuple.
"""
rendered_content: str = proto.Field(
proto.STRING,
number=1,
)
sdk_blob: bytes = proto.Field(
proto.BYTES,
number=2,
)
[docs]class GroundingChunk(proto.Message):
r"""Grounding chunk.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
Attributes:
web (google.ai.generativelanguage_v1.types.GroundingChunk.Web):
Grounding chunk from the web.
This field is a member of `oneof`_ ``chunk_type``.
"""
[docs] class Web(proto.Message):
r"""Chunk from the web.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
Attributes:
uri (str):
URI reference of the chunk.
This field is a member of `oneof`_ ``_uri``.
title (str):
Title of the chunk.
This field is a member of `oneof`_ ``_title``.
"""
uri: str = proto.Field(
proto.STRING,
number=1,
optional=True,
)
title: str = proto.Field(
proto.STRING,
number=2,
optional=True,
)
web: Web = proto.Field(
proto.MESSAGE,
number=1,
oneof="chunk_type",
message=Web,
)
[docs]class Segment(proto.Message):
r"""Segment of the content.
Attributes:
part_index (int):
Output only. The index of a Part object
within its parent Content object.
start_index (int):
Output only. Start index in the given Part,
measured in bytes. Offset from the start of the
Part, inclusive, starting at zero.
end_index (int):
Output only. End index in the given Part,
measured in bytes. Offset from the start of the
Part, exclusive, starting at zero.
text (str):
Output only. The text corresponding to the
segment from the response.
"""
part_index: int = proto.Field(
proto.INT32,
number=1,
)
start_index: int = proto.Field(
proto.INT32,
number=2,
)
end_index: int = proto.Field(
proto.INT32,
number=3,
)
text: str = proto.Field(
proto.STRING,
number=4,
)
[docs]class GroundingSupport(proto.Message):
r"""Grounding support.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
Attributes:
segment (google.ai.generativelanguage_v1.types.Segment):
Segment of the content this support belongs
to.
This field is a member of `oneof`_ ``_segment``.
grounding_chunk_indices (MutableSequence[int]):
A list of indices (into 'grounding_chunk') specifying the
citations associated with the claim. For instance [1,3,4]
means that grounding_chunk[1], grounding_chunk[3],
grounding_chunk[4] are the retrieved content attributed to
the claim.
confidence_scores (MutableSequence[float]):
Confidence score of the support references. Ranges from 0 to
1. 1 is the most confident. This list must have the same
size as the grounding_chunk_indices.
"""
segment: "Segment" = proto.Field(
proto.MESSAGE,
number=1,
optional=True,
message="Segment",
)
grounding_chunk_indices: MutableSequence[int] = proto.RepeatedField(
proto.INT32,
number=2,
)
confidence_scores: MutableSequence[float] = proto.RepeatedField(
proto.FLOAT,
number=3,
)
[docs]class EmbedContentRequest(proto.Message):
r"""Request containing the ``Content`` for the model to embed.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
Attributes:
model (str):
Required. The model's resource name. This serves as an ID
for the Model to use.
This name should match a model name returned by the
``ListModels`` method.
Format: ``models/{model}``
content (google.ai.generativelanguage_v1.types.Content):
Required. The content to embed. Only the ``parts.text``
fields will be counted.
task_type (google.ai.generativelanguage_v1.types.TaskType):
Optional. Optional task type for which the embeddings will
be used. Not supported on earlier models
(``models/embedding-001``).
This field is a member of `oneof`_ ``_task_type``.
title (str):
Optional. An optional title for the text. Only applicable
when TaskType is ``RETRIEVAL_DOCUMENT``.
Note: Specifying a ``title`` for ``RETRIEVAL_DOCUMENT``
provides better quality embeddings for retrieval.
This field is a member of `oneof`_ ``_title``.
output_dimensionality (int):
Optional. Optional reduced dimension for the output
embedding. If set, excessive values in the output embedding
are truncated from the end. Supported by newer models since
2024 only. You cannot set this value if using the earlier
model (``models/embedding-001``).
This field is a member of `oneof`_ ``_output_dimensionality``.
"""
model: str = proto.Field(
proto.STRING,
number=1,
)
content: gag_content.Content = proto.Field(
proto.MESSAGE,
number=2,
message=gag_content.Content,
)
task_type: "TaskType" = proto.Field(
proto.ENUM,
number=3,
optional=True,
enum="TaskType",
)
title: str = proto.Field(
proto.STRING,
number=4,
optional=True,
)
output_dimensionality: int = proto.Field(
proto.INT32,
number=5,
optional=True,
)
[docs]class ContentEmbedding(proto.Message):
r"""A list of floats representing an embedding.
Attributes:
values (MutableSequence[float]):
The embedding values.
"""
values: MutableSequence[float] = proto.RepeatedField(
proto.FLOAT,
number=1,
)
[docs]class EmbedContentResponse(proto.Message):
r"""The response to an ``EmbedContentRequest``.
Attributes:
embedding (google.ai.generativelanguage_v1.types.ContentEmbedding):
Output only. The embedding generated from the
input content.
"""
embedding: "ContentEmbedding" = proto.Field(
proto.MESSAGE,
number=1,
message="ContentEmbedding",
)
[docs]class BatchEmbedContentsRequest(proto.Message):
r"""Batch request to get embeddings from the model for a list of
prompts.
Attributes:
model (str):
Required. The model's resource name. This serves as an ID
for the Model to use.
This name should match a model name returned by the
``ListModels`` method.
Format: ``models/{model}``
requests (MutableSequence[google.ai.generativelanguage_v1.types.EmbedContentRequest]):
Required. Embed requests for the batch. The model in each of
these requests must match the model specified
``BatchEmbedContentsRequest.model``.
"""
model: str = proto.Field(
proto.STRING,
number=1,
)
requests: MutableSequence["EmbedContentRequest"] = proto.RepeatedField(
proto.MESSAGE,
number=2,
message="EmbedContentRequest",
)
[docs]class BatchEmbedContentsResponse(proto.Message):
r"""The response to a ``BatchEmbedContentsRequest``.
Attributes:
embeddings (MutableSequence[google.ai.generativelanguage_v1.types.ContentEmbedding]):
Output only. The embeddings for each request,
in the same order as provided in the batch
request.
"""
embeddings: MutableSequence["ContentEmbedding"] = proto.RepeatedField(
proto.MESSAGE,
number=1,
message="ContentEmbedding",
)
[docs]class CountTokensRequest(proto.Message):
r"""Counts the number of tokens in the ``prompt`` sent to a model.
Models may tokenize text differently, so each model may return a
different ``token_count``.
Attributes:
model (str):
Required. The model's resource name. This serves as an ID
for the Model to use.
This name should match a model name returned by the
``ListModels`` method.
Format: ``models/{model}``
contents (MutableSequence[google.ai.generativelanguage_v1.types.Content]):
Optional. The input given to the model as a prompt. This
field is ignored when ``generate_content_request`` is set.
generate_content_request (google.ai.generativelanguage_v1.types.GenerateContentRequest):
Optional. The overall input given to the ``Model``. This
includes the prompt as well as other model steering
information like `system
instructions <https://ai.google.dev/gemini-api/docs/system-instructions>`__,
and/or function declarations for `function
calling <https://ai.google.dev/gemini-api/docs/function-calling>`__.
``Model``\ s/``Content``\ s and
``generate_content_request``\ s are mutually exclusive. You
can either send ``Model`` + ``Content``\ s or a
``generate_content_request``, but never both.
"""
model: str = proto.Field(
proto.STRING,
number=1,
)
contents: MutableSequence[gag_content.Content] = proto.RepeatedField(
proto.MESSAGE,
number=2,
message=gag_content.Content,
)
generate_content_request: "GenerateContentRequest" = proto.Field(
proto.MESSAGE,
number=3,
message="GenerateContentRequest",
)
[docs]class CountTokensResponse(proto.Message):
r"""A response from ``CountTokens``.
It returns the model's ``token_count`` for the ``prompt``.
Attributes:
total_tokens (int):
The number of tokens that the ``Model`` tokenizes the
``prompt`` into. Always non-negative.
prompt_tokens_details (MutableSequence[google.ai.generativelanguage_v1.types.ModalityTokenCount]):
Output only. List of modalities that were
processed in the request input.
cache_tokens_details (MutableSequence[google.ai.generativelanguage_v1.types.ModalityTokenCount]):
Output only. List of modalities that were
processed in the cached content.
"""
total_tokens: int = proto.Field(
proto.INT32,
number=1,
)
prompt_tokens_details: MutableSequence[gag_content.ModalityTokenCount] = (
proto.RepeatedField(
proto.MESSAGE,
number=6,
message=gag_content.ModalityTokenCount,
)
)
cache_tokens_details: MutableSequence[gag_content.ModalityTokenCount] = (
proto.RepeatedField(
proto.MESSAGE,
number=7,
message=gag_content.ModalityTokenCount,
)
)
__all__ = tuple(sorted(__protobuf__.manifest))
